Recognizing satellite components is very important for on-orbit service tasks such as space debris removal and orbital refueling, but it is also a challenging task. This paper proposes a satellite component recognition method based on improved YOLOv10 to address the issues of complex lighting variations and dynamic Earth backgrounds in space environments. This method improves the feature selection and representation ability of the model by introducing the ConvLSTM module to improve the recognition accuracy. It also simplifies the model size and computational complexity by using lightweight strategies to improve recognition efficiency. A public dataset and a self-made dataset are used to test the proposed method, and the results demonstrate that the proposed method can effectively recognize satellite typical components such as the satellite body, solar panels, antennas, tripods, nozzles, and payloads, and has good recognition performance.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Satellite Component Recognition Method Based on Improved YOLOv10


    Beteiligte:
    Huang, Haoran (Autor:in) / Ning, Tingting (Autor:in) / Shi, Xinran (Autor:in) / Bo, Yuming (Autor:in)


    Erscheinungsdatum :

    16.05.2025


    Format / Umfang :

    1037691 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    BRA-YOLOv10: UAV Small Target Detection Based on YOLOv10

    Quanyu Zhang / Xin Wang / Heng Shi et al. | DOAJ | 2025

    Freier Zugriff

    Anti-Interference and Multi-Scale Ship Detection Algorithm Based on YOLOv10

    Liang, Huimin / Li, Qinlin / Liao, Kefei et al. | IEEE | 2025


    Challenges and Advances in UAV-Based Vehicle Detection Using YOLOv9 and YOLOv10

    Bakirci, Murat / Dmytrovych, Petro / Bayraktar, Irem et al. | IEEE | 2024